Set up combined data of Mutect2 and Strelka2

Candace Savonen - CCDL for ALSF

This notebook sets up the MAF data files as a combined file for ready comparison in 02-analyze-concordance.Rmd and also does some first line analyses from ready-made maftools functions.

It is the first notebook in this series which addresses issue # 30 in OpenPBTA.

Summary of the Set Up:

For both the Strelka2 and MuTect2 datasets, the data is imported by maftools::read.maf and the corresponding clinical data from pbta-histologies.tsv is added to the object. This is only done once as written (as the read.maf is very memory intensive) and each MuTect2 and Strelka2 are saved to an RDS file for faster and ready reloading.

For both Strelka2 and MuTect2 data, the following variables are calculated and added into the combined dataset for analysis in 02-analyze-concordance.Rmd.

Variables created:

• vaf : Variant Allele Frequency = (t_alt_count) / (t_ref_count + t_alt_count).
  
• mutation_id: Used to determine whether two variants calls are identical in both datasets. It is a concatenation of: Hugo_Symbol, change, Start_Position, and Tumor_Sample_Barcode (the sample ID).
  
• base_change: variable that indicates the exact change in bases (e.g. ‘T>C’).
  
• change: indicates the base_change information but groups together deletions, insertions, and long (more than a SNV) as their own groups.
  
• coding: Summarize the BIOTYPE variable for whether or not it is a coding gene.

The output files from this notebook:

• scratch/strelka2.RDS
  
• scratch/mutect2.RDS
  
• scratch/metadata_filtered_maf_samples.tsv
  
• analyses/mutect2-vs-strelka2/plots/sample_cor_mutect2_vs_strelka2.pdf
• analyses/mutect2-vs-strelka2/plots/sample_cor_mutect2_vs_strelka2.pdf
• analyses/mutect2-vs-strelka2/results/combined_results.tsv

Usage

To run this from the command line, use:

Rscript -e "rmarkdown::render('analyses/mutect2-vs-strelka2/01-set-up.Rmd', 
                              clean = TRUE)"

This assumes you are in the top directory of the repository.

Set Up

# We need maftools - this will be added to the running Docker issue whenever it is up
if (!("maftools" %in% installed.packages())) {
  if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
  }
  BiocManager::install("maftools")
}

Get magrittr pipe

`%>%` <- dplyr::`%>%`

Directories and files

Path to the symlinked data obtained via bash download-data.sh.

data_dir <- file.path("..", "..", "data")
scratch_dir <- file.path("..", "..", "scratch")

Create output directories in this analysis folder.

if (!dir.exists("results")) {
  dir.create("results")
}
if (!dir.exists("plots")) {
  dir.create("plots")
}

Read in the metadata information

Running maftools::read.maf takes a lot of computing power and time, so to avoid having to run this for both datasets everytime we want to re-run this notebook or the analyses in the other notebook, I’ve set this up to save the MAF objects as RDS files.

First let’s establish the file paths.

# File paths for the needed files for this analysis
metadata_dir <- file.path(scratch_dir, "metadata_filtered_maf_samples.tsv")
strelka2_dir <- file.path(scratch_dir, "strelka2.RDS")
mutect2_dir <- file.path(scratch_dir, "mutect2.RDS")

Read in the Strelka2 and Mutect2 data

Will read in the data as maftools objects from an RDS file, unless maftools has not been run on them yet. We will use metadata as the clinicalData for the maftools object.

Note: If you trying to run the initialset up step in a Docker container, it will likely be out of memory killed, unless you have ~50GB you can allot to Docker.

# Get a vector of whether these exist
files_needed <- file.exists(metadata_dir, strelka2_dir, mutect2_dir)

if (all(files_needed)) {
  # Read the ready-to-go files if these files exist
  metadata <- metadata <- readr::read_tsv(metadata_dir)
  strelka2 <- readRDS(strelka2_dir)
  mutect2 <- readRDS(mutect2_dir)
} else { # If any of the needed files don't exist, rerun this process:
  # Only import the sample names
  strelka2_samples <- data.table::fread(file.path(
    data_dir,
    "pbta-snv-strelka2.vep.maf.gz"
  ),
  select = "Tumor_Sample_Barcode",
  skip = 1,
  data.table = FALSE
  )

  mutect2_samples <- data.table::fread(file.path(
    data_dir,
    "pbta-snv-mutect2.vep.maf.gz"
  ),
  select = "Tumor_Sample_Barcode",
  skip = 1,
  data.table = FALSE
  )

  # Isolate metadata to only the samples that are in the datasets
  metadata <- readr::read_tsv(data_dir, "pbta-histologies.tsv") %>%
    dplyr::filter(Kids_First_Biospecimen_ID %in% c(strelka2_samples, mutect2_samples)) %>%
    dplyr::distinct(Kids_First_Biospecimen_ID, .keep_all = TRUE) %>%
    dplyr::arrange() %>%
    readr::write_tsv(file.path(scratch_dir, "metadata_filtered_maf_samples.tsv"))

  # Read in original strelka file with maftools
  strelka <- maftools::read.maf(file.path(data_dir, "pbta-snv-strelka2.vep.maf.gz"),
    clinicalData = metadata
  )

  # Save to RDS so we don't have to run this again
  saveRDS(strelka, strelka2_dir)

  # Same for MuTect2
  mutect2 <- maftools::read.maf(file.path(data_dir, "pbta-snv-mutect2.vep.maf.gz"),
    clinicalData = metadata
  )
  saveRDS(mutect2, mutect2_dir)
}

Get summaries and write them to TSVs

Get gene summaries and write to TSV files.

strelka2_gene_sum <- maftools::getGeneSummary(strelka2) %>%
  readr::write_tsv(file.path(
    "results",
    "strelka2_gene_summary.tsv"
  ))

mutect2_gene_sum <- maftools::getGeneSummary(mutect2) %>%
  readr::write_tsv(file.path(
    "results",
    "mutect2_gene_summary.tsv"
  ))

Get sample summaries and write to TSV files.

strelka2_sample_sum <- maftools::getSampleSummary(strelka2) %>%
  readr::write_tsv(file.path(
    "results",
    "strelka2_sample_summary.tsv"
  ))

mutect2_sample_sum <- maftools::getSampleSummary(mutect2) %>%
  readr::write_tsv(file.path(
    "results",
    "mutect2_sample_summary.tsv"
  ))

Number of mutations per gene correlation

combined_gene <- mutect2_gene_sum %>%
  dplyr::full_join(strelka2_gene_sum, by = "Hugo_Symbol") %>%
  reshape2::melt(id = "Hugo_Symbol") %>%
  dplyr::mutate(dataset = as.character(grepl(".x$", variable))) %>%
  dplyr::mutate(dataset = dplyr::recode(dataset,
    `TRUE` = "mutect2",
    `FALSE` = "strelka2"
  )) %>%
  dplyr::mutate(variable = gsub(".x$|.y$", "", variable)) %>%
  tidyr::spread("dataset", "value")

Let’s get a correlation test on the genes overall.

cor.test(combined_gene$mutect2, combined_gene$strelka2, method = "spearman")

Cannot compute exact p-value with ties

    Spearman's rank correlation rho

data:  combined_gene$mutect2 and combined_gene$strelka2
S = 4.7233e+13, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.9568251 

cor.test(combined_gene$mutect2, combined_gene$strelka2, method = "pearson")

    Pearson's product-moment correlation

data:  combined_gene$mutect2 and combined_gene$strelka2
t = 567.61, df = 187234, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7935980 0.7969277
sample estimates:
      cor 
0.7952689 

Number of mutations per sample correlation.

combined_sample <- mutect2_sample_sum %>%
  dplyr::full_join(strelka2_sample_sum, by = "Tumor_Sample_Barcode") %>%
  reshape2::melt(id = "Tumor_Sample_Barcode") %>%
  dplyr::mutate(dataset = as.character(grepl(".x$", variable))) %>%
  dplyr::mutate(dataset = dplyr::recode(dataset,
    `TRUE` = "mutect2",
    `FALSE` = "strelka2"
  )) %>%
  dplyr::mutate(variable = gsub(".x$|.y$", "", variable)) %>%
  tidyr::spread("dataset", "value")

Let’s get a correlation test on the genes overall.

cor.test(combined_sample$mutect2, combined_sample$strelka2, method = "spearman")

Cannot compute exact p-value with ties

    Spearman's rank correlation rho

data:  combined_sample$mutect2 and combined_sample$strelka2
S = 3.3543e+10, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.7808425 

cor.test(combined_sample$mutect2, combined_sample$strelka2, method = "pearson")

    Pearson's product-moment correlation

data:  combined_sample$mutect2 and combined_sample$strelka2
t = 778.22, df = 9718, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9917519 0.9923801
sample estimates:
      cor 
0.9920722 

Plot Transition/Transversions

maftools::plotTiTv(maftools::titv(strelka2))

maftools::plotTiTv(maftools::titv(mutect2))

Set up new variables

Here we will make these new variables for both Mutect2 and Strelka2 dataset: - Calculate VAF for each. The VAFs for each variation in each dataset are calculated by t_alt_count) / (t_ref_count + t_alt_count), as following the code used in maftools
- Create a base_change variable that indicates the exact change in bases.
- Create a change variable for each which indicates the base_change information but groups together deletions, insertions, and long (more than a SNV) as their own groups.
- Make a mutation id by concatenating Hugo_Symbol, change, Start_Position, and Tumor_Sample_Barcode (the sample ID).
We will use this variable to determine whether two variants calls are identical in both datasets. - Summarize the BIOTYPE variable for whether or not it is a coding gene.

Let’s set up Strelka2’s variables first.

strelka2_vaf <- strelka2@data %>%
  dplyr::mutate(
    vaf = as.numeric(t_alt_count) / (as.numeric(t_ref_count) +
      as.numeric(t_alt_count)),
    base_change = paste0(Reference_Allele, ">", Allele),
    coding = dplyr::case_when(
      BIOTYPE != "protein_coding" ~ "non-coding",
      TRUE ~ "protein_coding"
    )
  ) %>%
  dplyr::mutate(change = dplyr::case_when(
    grepl("^-", base_change) ~ "insertion",
    grepl("-$", base_change) ~ "deletion",
    nchar(base_change) > 3 ~ "long_change",
    TRUE ~ base_change
  )) %>%
  dplyr::mutate(
    mutation_id = paste0(
      Hugo_Symbol, "_",
      change, "_",
      Start_Position, "_",
      Tumor_Sample_Barcode
    )
  ) %>%
  dplyr::select(-which(apply(is.na(.), 2, all)))

NAs introduced by coercion

# Take a look at this df
strelka2_vaf

Now we will do the same for MuTect2.

mutect2_vaf <- mutect2@data %>%
  dplyr::mutate(
    vaf = as.numeric(t_alt_count) / (as.numeric(t_ref_count) +
      as.numeric(t_alt_count)),
    base_change = paste0(Reference_Allele, ">", Allele),
    coding = dplyr::case_when(
      BIOTYPE != "protein_coding" ~ "non-coding",
      TRUE ~ "protein_coding"
    )
  ) %>%
  dplyr::mutate(change = dplyr::case_when(
    grepl("^-", base_change) ~ "insertion",
    grepl("-$", base_change) ~ "deletion",
    nchar(base_change) > 3 ~ "long_change",
    TRUE ~ base_change
  )) %>%
  dplyr::mutate(
    mutation_id = paste0(
      Hugo_Symbol, "_",
      change, "_",
      Start_Position, "_",
      Tumor_Sample_Barcode
    )
  ) %>%
  dplyr::select(-which(apply(is.na(.), 2, all)))

# Take a look at this df
mutect2_vaf

Combine MuTect2 and Strelka2 data.frames into one data.frame

Join these datasets based on the mutation_id created above. Save to a TSV file to be used in the subsequent notebook.

# Merge these data.frames together
vaf_df <- strelka2_vaf %>%
  dplyr::full_join(mutect2_vaf,
    by = "mutation_id",
    suffix = c(".strelka2", ".mutect2")
  ) %>%
  # Make a variable that denotes which dataset it is in.
  dplyr::mutate(dataset = dplyr::case_when(
    is.na(Allele.mutect2) ~ "strelka2_only",
    is.na(Allele.strelka2) ~ "mutect2_only",
    TRUE ~ "both"
  )) %>%
  readr::write_tsv(file.path("results", "combined_results.tsv"))

Make a zipped up version that can be stored on GitHub.

zip(file.path("results", "combined_results.tsv.zip"),
    file.path("results", "combined_results.tsv"))

  adding: results/combined_results.tsv (deflated 85%)

Session Info:

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.5

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] Biobase_2.44.0      BiocGenerics_0.30.0

loaded via a namespace (and not attached):
 [1] pkgload_1.0.2        tidyr_0.8.3          splines_3.6.1        jsonlite_1.6         foreach_1.4.7       
 [6] assertthat_0.2.1     BiocManager_1.30.4   yaml_2.2.0           remotes_2.1.0        sessioninfo_1.1.1   
[11] lattice_0.20-38      pillar_1.4.2         backports_1.1.4      glue_1.3.1           digest_0.6.20       
[16] RColorBrewer_1.1-2   colorspace_1.4-1     Matrix_1.2-17        htmltools_0.3.6      plyr_1.8.4          
[21] pkgconfig_2.0.2      devtools_2.1.0       bibtex_0.4.2         purrr_0.3.2          xtable_1.8-4        
[26] scales_1.0.0         processx_3.4.1       VennDiagram_1.6.20   tibble_2.1.3         pkgmaker_0.27       
[31] styler_1.1.1.9003    ggplot2_3.2.1        usethis_1.5.1        withr_2.1.2          hexbin_1.27.3       
[36] lazyeval_0.2.2       cli_1.1.0            survival_2.44-1.1    magrittr_1.5         crayon_1.3.4        
[41] memoise_1.1.0        evaluate_0.14        ps_1.3.0             fs_1.3.1             doParallel_1.0.15   
[46] NMF_0.21.0           pkgbuild_1.0.4       tools_3.6.1          registry_0.5-1       data.table_1.12.2   
[51] prettyunits_1.0.2    hms_0.5.0            formatR_1.7          gridBase_0.4-7       stringr_1.4.0       
[56] munsell_0.5.0        cluster_2.1.0        rngtools_1.4         lambda.r_1.2.3       maftools_2.0.15     
[61] callr_3.3.1          compiler_3.6.1       rlang_0.4.0          futile.logger_1.4.3  grid_3.6.1          
[66] iterators_1.0.12     rstudioapi_0.10      labeling_0.3         colorblindr_0.1.0    base64enc_0.1-3     
[71] rmarkdown_1.14       testthat_2.2.1       gtable_0.3.0         codetools_0.2-16     curl_4.0            
[76] rematch2_2.1.0       reshape2_1.4.3       R6_2.4.0             knitr_1.24           dplyr_0.8.3         
[81] zeallot_0.1.0        rprojroot_1.3-2      futile.options_1.0.1 readr_1.3.1          desc_1.2.0          
[86] stringi_1.4.3        Rcpp_1.0.2           vctrs_0.2.0          wordcloud_2.6        tidyselect_0.2.5    
[91] xfun_0.8